More illustrative investigation on window-shaped obstacle avoidance of robot manipulators using a simplified LVI-based primal-dual neural network

Abstract

One important issue in the motion planning of kinematic redundant manipulators is the online obstacle-avoidance. For such purposes, we proposed and unified the scheme formulation based on general quadratic-programs (QP), which incorporates physical constraints such as joint physical limits and collision-avoidance inequality. In this paper, a simplified primal-dual neural network based on linear variational inequalities (LVI) is presented for the real-time solution of such a collision-free inverse-kinematic planning scheme. The neural network solves the strictly-convex QP in an inverse-free manner, in addition to the simple piecewise-linear dynamics and global exponential convergence to optimal solutions. Further computer-simulations based on PA10 redundant robot manipulator substantiate the efficacy of the scheme formulation and its neural-network solver on window-shaped obstacle avoidance.